3 results
Moving Beyond Contact Precautions: Implementation of a Staphylococcus aureus Screening and Decolonization Program
- Sarah Hochman, Anna Stachel, Michael Phillips, Stephanie Sterling, Jennifer Lighter, Maria Aguero-Rosenfeld, Tamara King-Morrieson
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- Journal:
- Infection Control & Hospital Epidemiology / Volume 41 / Issue S1 / October 2020
- Published online by Cambridge University Press:
- 02 November 2020, pp. s321-s322
- Print publication:
- October 2020
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Background:Staphylococcus aureus–colonized hospitalized patients are at risk for invasive infection and can transmit S. aureus to other patients in the absence of symptoms. Infection isolation precautions do not reduce the risk of infection in colonized patients and are untenable in health systems with high rates of S. aureus colonization. Objective: We implemented an inpatient S. aureus screening and targeted decolonization program across hospital campuses to reduce transmission and invasive infection. We screen and decolonize for methicillin-susceptible S. aureus (MSSA) and methicillin-resistant S. aureus (MRSA) because MSSA makes up more than half of all S. aureus isolated from clinical cultures in our health system. Methods: All medicine, pediatrics, and transplant patients receive S. aureus nares culture at admission and upon change in level of care for medicine, and at admission and weekly for pediatrics and transplant patients. All S. aureus–colonized patients receive decolonization with nasal mupirocin ointment and chlorhexidine baths. Two implementation frameworks guide our processes for S. aureus screening and decolonization: the Consolidated Framework for Implementation Research, to evaluate factors affecting implementation at different levels of the health system, and the Dynamic Sustainability Framework, to account for iterative changes as the hospital setting and patient population change over time. Implementation interventions focus on education of patients and bedside nurses who perform S. aureus screening and decolonization; utilization of the electronic health record to identify patients for screening and/or decolonization and avoid human error; and introduction of a clinical nurse specialist to oversee the program and to provide iterative feedback. Results: At baseline, 21% of patients had S. aureus colonization, 20% of which was MRSA, and the MRSA bloodstream infection rate was 0.06 per 1,000 patient days. After program implementation, there was no change in S. aureus colonization and the MRSA bloodstream infection rate fell to 0.04 per 1,000 patient days. Screening compliance improved from 39% (N = 1,805) of eligible patients in the 6-month period before the introduction of the clinical nurse specialist to 52% (N = 2,024) after the introduction of the clinical nurse specialist. In the same periods, decolonization increased from 18.6% to 41% of eligible patients. Conclusions: We used 2 implementation frameworks to design our S. aureus screening and decolonization program and to make iterative changes to the program as it evolved to include new patient populations and different hospital settings. This resulted in a large-scale, sustainable, health system program for S. aureus control that avoids reliance on infection isolation precautions.
Funding: None
Disclosures: None
Data Mining to Guide a Program to Prevent Infection Related Readmissions From Skilled Nursing Facilities
- Anna Stachel, Julie Klock, Dan Ding, Jennifer Lighter, Kwesi Daniel, Levi Waldron
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- Journal:
- Infection Control & Hospital Epidemiology / Volume 41 / Issue S1 / October 2020
- Published online by Cambridge University Press:
- 02 November 2020, pp. s29-s30
- Print publication:
- October 2020
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Background: Readmissions to hospitals are common, costly and often preventable, notably readmissions due to infections. A 30-day readmission analysis following hospital discharges, found much of the variation in Medicare spending between hospitals was related to readmissions and skilled nursing facility (SNF) care. Although some readmissions of patients with advanced disease are not preventable, efforts to decrease readmission are most effectively directed towards those patients with intermediate levels of a specific risk. A prediction model to identify patients at highest (or intermediate) risk of infection readmission will help healthcare administrators and providers to allocate appropriate resources. Hospitals should use electronic health record (EHR) data with modern data mining techniques to create more curated, sophisticated models as part of a comprehensive transitional care program. We propose using the risk estimates of a validated prediction model to notify stakeholders and develop readmission rate reports by SNF or discharging physician. Methods: We applied machine learning (ML) methods to predict the risk of 30-day readmission due to sepsis and pneumonia of patients discharged to SNF. We used our EHR data during 2012–2017 to train and data from 2018 to validate. We applied ML algorithms to data including logistic regression, random forest, gradient boosting trees, and support vector machine. Data from EDW and EPIC clarity tables were extracted and managed using SAS Base 9.4 and Enterprise Miner 14.3 (SAS Institute, Cary, NC). We assessed the discrimination and calibration to select the most effective prediction model. Using the resulted risk estimates, we created a notification system and reports for key stakeholders. Results: Figures 1 and 2 show the discrimination and calibration results of the final selected gradient boosting model (GBM). For predicting unplanned readmissions with sepsis and with pneumonia within 30 days after discharge to SNF, the c-statistic for final GBM model with 140 features was 0.69 (95% CI 0.65-0.73) and 73 features was 0.71 (95% CI 0.66-0.75), respectively. Table 1 lists features important to the validation set of the prediction model. We used estimates from these models to develop a daily email notification of patients discharged to SNF stratified into a low, medium, and high risk group for sepsis and pneumonia. We additionally created reports with case-mix adjustments to benchmark SNFs and discharging physicians to monitor and understand performance. Conclusions: Hospitals should leverage the plethora of data found in EHRs to curate readmission prediction models, and promote collaboration among transitional care teams and IPC to ultimately reduce readmissions due to sepsis and pneumonia.
Funding: None
Disclosures: None
Use of Varying Single-Nucleotide Polymorphism Thresholds to Identify Strong Epidemiologic Links Among Patients with Methicillin-Resistant Staphylococcus aureus (MRSA)
- Ioannis Zacharioudakis, Dan Ding, Fainareti Zervou, Anna Stachel, Sarah Hochman, Stephanie Sterling, Jennifer Lighter, Maria Aguero-Rosenfeld, Bo Shopsin, Michael Phillips
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- Journal:
- Infection Control & Hospital Epidemiology / Volume 41 / Issue S1 / October 2020
- Published online by Cambridge University Press:
- 02 November 2020, pp. s423-s424
- Print publication:
- October 2020
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Background: Whole-genome sequencing (WGS) has a high discriminatory power in confirming outbreaks. Outbreak investigation models that categorize the possibility of an outbreak based on the degree of genetic relatedness of isolates are highly dependent on the single-nucleotide polymorphism (SNP) threshold used. Methods: NYU Langone Medical center is a 725-bed academic center that has implemented WGS of methicillin-resistant Staphylococcus aureus (MRSA) isolates since 2016. Patients admitted to a medical or intensive care unit were screened on admission and transfer. The first surveillance and clinical MRSA isolate during each hospitalization was sequenced. We conducted a retrospective analysis to identify strong epidemiologic links among patients involved in genetically related clusters. We used different SNP thresholds to define genetic relatedness to identify the optimal threshold that should prompt an outbreak investigation. We considered strong hospital epidemiologic links sharing the same room or unit or having resided in the same room or unit within 7 days. A pairwise analysis was conducted to compare the epidemiologic links among patients involved in genetically related clusters. Results: Among 1,070 isolates, our analysis focused on 777 belonging to USA100 and USA300 clones. For USA100 isolates, we identified 8, 14, and 20 clusters comprising of 16, 29, and 42 patients when the threshold for genetic relatedness was set at 20, 40, and 60 SNP differences, respectively. Patients identified in a cluster yielded a strong hospital epidemiologic link in 62.5%, 87.5%, and 91.7% of cases (Fig. 1). For USA300 isolates, SNP differences of 10, 20, and 30 were used, identifying 20, 34, and 40 clusters of 43, 79, and 127 patients. The expansion of the threshold from 10 to 30 resulted in a decrease of the percentage of pairwise analyses with a strong hospital epidemiologic link from 57.7% to 13.6% by increasing 13-fold the number of analyses that were conducted to identify only 3 times more cases with strong epidemiologic links (Fig. 2). Conclusions: The results of our study indicate that SNPs thresholds determined by intrapatient variability of MRSA isolates might need to be tailored to the individual setting to guide infection control interventions because optimal thresholds might vary depending on characteristics of the population, MRSA isolates, and screening practices. Establishing conservative thresholds might allow the identification and quantification over time of the locations (eg, rooms or units) where transmission is occurring as well as the investigation of the clusters without strong epidemiologic links that might be valuable in elucidating unrecognized routes of transmission.
Funding: None
Disclosures: None